Master Thesis - Department of Computer Science

lution face recognition [36] for Yale, PIE and ORL databases based on the successful
subbands determined on testing set. From the results given in Table 3.7, we can
observe that the process of decision fusion based on sum and product rules perform
the best among all decision combination strategies proposed in [36]. However, in case
of ORL, this strategy fails to improve upon that provided by the gray-level image
using PCA.
In the second case of experimentation, the successful subbands are selected using
the validation set, as used in our proposed technique (see Table 3.1). Results are
given in Table 3.8-3.9, in a similar manner, as in Table 3.6-3.7. We can notice that
the over-tuning done in the first case offers apparently better results, by comparing
the performances given in Table 3.7 (first case) and the corresponding top seven rows
of Table 3.9 (second case).
Table 3.6: Best performing and successful subbands for Ekenel’s multiresolution face
recognition [36] for Yale, PIE and ORL databases determined on testing set.
Yale PIE ORL
Best Performing Subband V3 HA2 A2
A1, V2, V3 A1, H1, V1, D1 A1, A2, A3
Successful Subbands H2, V2, D2
Selected for Data, H3, V3, D3
Feature, Decision HA3, HH3, HV3, HD3
Fusion V A3, V H3, V V3, V D3
DA3, DH3, DV3, DD3
Hence, to provide a meaningful comparative study, which is also practically use-
ful, we use the second approach (based on validation set) to compare the performance
of our underlying subband face representation with that suggested in [36], using data,
feature and decision level fusion. We do so with identical feature extraction and dis-
tance metric, which is PCA and L2 norm in both cases. Image size is also kept as
same for both cases. We use the same validation set to select the subbands performing
equally good and better than original image for multiresolution face recognition. The
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